Week 10 Assignment: CVA Data Exploration¶

Group Name: Extreme Heat in Los Angeles¶

This week, I will attempt to explore the CVA vulnerability data for the final and create some sort of index. Author: Amanda Gormsen

In [1]:
# to manipulate data
import pandas as pd

# for spatial data
import geopandas as gpd

# for plotting
import matplotlib.pyplot as plt

# for network analysis
import networkx as nx

# for street network analysis
import osmnx as ox

# for basemaps
import contextily as ctx

# for interactive webmaps
import folium
In [2]:
!pip install pysal

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In [3]:
# Importing pysal to reclassify data
import pysal as ps
import mapclassify
n_classes = 5
In [4]:
# Importing data
df=gpd.read_file('Los_Angeles_County_CVA_Social_Vulnerability_Index.geojson')

These are the variables that the CVA dataset considers for social vulnerability:

Children Older_Adults Older_Adults_Living_Alone Limited_English No_High_School_Diploma Female Female_Householder Asthma Cardiovascular_Disease Disability No_Health_Insurance Living_in_Group_Quarters Mobile_Homes Rent_Burden Renters Median_Income Poverty Households_Without_Vehicle_Acce Transit_Access Outdoor_Workers Unemployed Foreign_Born Library_Access No_Internet_Subscription Voter_Turnout_Rate Hispanic_Latinx White Black Native_Tribal Asian NHOPI Other_Race Two_or_More_Races SoVI_Score SoVI_Thirds

I want to focus on the variables that seem most related to extreme heat vulnerability, esp related to cooling centers and transit. These are the variables I will use in my custom index:

  • Poverty (values = 0 to 83)
  • Households_Without_Vehicle_Acce (values = 0 to 85)
  • Transit_Access (values = 0 to 100)
  • Outdoor_Workers (values = 0 to 32)
  • Library_Access (values = 0 to 4)
In [5]:
# Trimming data
df_trimmed=df[['Census_Tract','Poverty', 'Households_Without_Vehicle_Acce', 'Transit_Access', 'Outdoor_Workers', 'Library_Access','geometry']]
In [6]:
classifier=mapclassify.NaturalBreaks.make(k=5)
In [7]:
df_trimmed['poverty_classes']=df_trimmed[["Poverty"]].apply(classifier)
df_trimmed

/opt/conda/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[7]:
Census_Tract Poverty Households_Without_Vehicle_Acce Transit_Access Outdoor_Workers Library_Access geometry poverty_classes
0 Census Tract 5709.02 1.5 3.8 0.0 5.6 0.8 POLYGON ((-118.12509 33.86032, -118.12287 33.8... 0
1 Census Tract 5715.02 10.0 9.0 98.3 2.5 1.2 POLYGON ((-118.19274 33.84376, -118.18975 33.8... 0
2 Census Tract 9106.05 17.1 3.3 0.0 11.6 2.9 POLYGON ((-118.07616 34.57281, -118.06728 34.5... 1
3 Census Tract 9107.12 7.0 0.9 0.0 3.6 4.2 POLYGON ((-118.05857 34.55809, -118.05847 34.5... 0
4 Census Tract 9107.15 11.3 8.9 0.0 4.6 3.1 POLYGON ((-118.08506 34.55654, -118.08485 34.5... 1
... ... ... ... ... ... ... ... ...
2322 Census Tract 6010.02 19.6 12.4 100.0 7.1 0.7 POLYGON ((-118.35263 33.95271, -118.35208 33.9... 2
2323 Census Tract 9005.01 28.7 4.9 0.0 7.3 1.5 POLYGON ((-118.13480 34.68936, -118.12968 34.6... 2
2324 Census Tract 9200.29 5.4 3.7 0.0 2.7 1.6 POLYGON ((-118.50373 34.42607, -118.50050 34.4... 0
2325 Census Tract 5420 13.3 8.7 88.0 9.3 0.7 POLYGON ((-118.20731 33.90754, -118.20641 33.9... 1
2326 Census Tract 1244 12.9 6.4 0.0 2.3 1.4 POLYGON ((-118.41379 34.17940, -118.41160 34.1... 1

2327 rows × 8 columns

In [8]:
df_trimmed['vehicleaccess_classes']=df_trimmed[["Households_Without_Vehicle_Acce"]].apply(classifier)
df_trimmed

/opt/conda/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[8]:
Census_Tract Poverty Households_Without_Vehicle_Acce Transit_Access Outdoor_Workers Library_Access geometry poverty_classes vehicleaccess_classes
0 Census Tract 5709.02 1.5 3.8 0.0 5.6 0.8 POLYGON ((-118.12509 33.86032, -118.12287 33.8... 0 0
1 Census Tract 5715.02 10.0 9.0 98.3 2.5 1.2 POLYGON ((-118.19274 33.84376, -118.18975 33.8... 0 1
2 Census Tract 9106.05 17.1 3.3 0.0 11.6 2.9 POLYGON ((-118.07616 34.57281, -118.06728 34.5... 1 0
3 Census Tract 9107.12 7.0 0.9 0.0 3.6 4.2 POLYGON ((-118.05857 34.55809, -118.05847 34.5... 0 0
4 Census Tract 9107.15 11.3 8.9 0.0 4.6 3.1 POLYGON ((-118.08506 34.55654, -118.08485 34.5... 1 1
... ... ... ... ... ... ... ... ... ...
2322 Census Tract 6010.02 19.6 12.4 100.0 7.1 0.7 POLYGON ((-118.35263 33.95271, -118.35208 33.9... 2 2
2323 Census Tract 9005.01 28.7 4.9 0.0 7.3 1.5 POLYGON ((-118.13480 34.68936, -118.12968 34.6... 2 0
2324 Census Tract 9200.29 5.4 3.7 0.0 2.7 1.6 POLYGON ((-118.50373 34.42607, -118.50050 34.4... 0 0
2325 Census Tract 5420 13.3 8.7 88.0 9.3 0.7 POLYGON ((-118.20731 33.90754, -118.20641 33.9... 1 1
2326 Census Tract 1244 12.9 6.4 0.0 2.3 1.4 POLYGON ((-118.41379 34.17940, -118.41160 34.1... 1 1

2327 rows × 9 columns

In [9]:
df_trimmed['transitaccess_classes']=df_trimmed[["Transit_Access"]].apply(classifier)
df_trimmed

/opt/conda/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[9]:
Census_Tract Poverty Households_Without_Vehicle_Acce Transit_Access Outdoor_Workers Library_Access geometry poverty_classes vehicleaccess_classes transitaccess_classes
0 Census Tract 5709.02 1.5 3.8 0.0 5.6 0.8 POLYGON ((-118.12509 33.86032, -118.12287 33.8... 0 0 0
1 Census Tract 5715.02 10.0 9.0 98.3 2.5 1.2 POLYGON ((-118.19274 33.84376, -118.18975 33.8... 0 1 4
2 Census Tract 9106.05 17.1 3.3 0.0 11.6 2.9 POLYGON ((-118.07616 34.57281, -118.06728 34.5... 1 0 0
3 Census Tract 9107.12 7.0 0.9 0.0 3.6 4.2 POLYGON ((-118.05857 34.55809, -118.05847 34.5... 0 0 0
4 Census Tract 9107.15 11.3 8.9 0.0 4.6 3.1 POLYGON ((-118.08506 34.55654, -118.08485 34.5... 1 1 0
... ... ... ... ... ... ... ... ... ... ...
2322 Census Tract 6010.02 19.6 12.4 100.0 7.1 0.7 POLYGON ((-118.35263 33.95271, -118.35208 33.9... 2 2 4
2323 Census Tract 9005.01 28.7 4.9 0.0 7.3 1.5 POLYGON ((-118.13480 34.68936, -118.12968 34.6... 2 0 0
2324 Census Tract 9200.29 5.4 3.7 0.0 2.7 1.6 POLYGON ((-118.50373 34.42607, -118.50050 34.4... 0 0 0
2325 Census Tract 5420 13.3 8.7 88.0 9.3 0.7 POLYGON ((-118.20731 33.90754, -118.20641 33.9... 1 1 3
2326 Census Tract 1244 12.9 6.4 0.0 2.3 1.4 POLYGON ((-118.41379 34.17940, -118.41160 34.1... 1 1 0

2327 rows × 10 columns

In [10]:
df_trimmed['outdoorwork_classes']=df_trimmed[["Outdoor_Workers"]].apply(classifier)
df_trimmed

/opt/conda/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[10]:
Census_Tract Poverty Households_Without_Vehicle_Acce Transit_Access Outdoor_Workers Library_Access geometry poverty_classes vehicleaccess_classes transitaccess_classes outdoorwork_classes
0 Census Tract 5709.02 1.5 3.8 0.0 5.6 0.8 POLYGON ((-118.12509 33.86032, -118.12287 33.8... 0 0 0 1
1 Census Tract 5715.02 10.0 9.0 98.3 2.5 1.2 POLYGON ((-118.19274 33.84376, -118.18975 33.8... 0 1 4 0
2 Census Tract 9106.05 17.1 3.3 0.0 11.6 2.9 POLYGON ((-118.07616 34.57281, -118.06728 34.5... 1 0 0 3
3 Census Tract 9107.12 7.0 0.9 0.0 3.6 4.2 POLYGON ((-118.05857 34.55809, -118.05847 34.5... 0 0 0 1
4 Census Tract 9107.15 11.3 8.9 0.0 4.6 3.1 POLYGON ((-118.08506 34.55654, -118.08485 34.5... 1 1 0 1
... ... ... ... ... ... ... ... ... ... ... ...
2322 Census Tract 6010.02 19.6 12.4 100.0 7.1 0.7 POLYGON ((-118.35263 33.95271, -118.35208 33.9... 2 2 4 2
2323 Census Tract 9005.01 28.7 4.9 0.0 7.3 1.5 POLYGON ((-118.13480 34.68936, -118.12968 34.6... 2 0 0 2
2324 Census Tract 9200.29 5.4 3.7 0.0 2.7 1.6 POLYGON ((-118.50373 34.42607, -118.50050 34.4... 0 0 0 0
2325 Census Tract 5420 13.3 8.7 88.0 9.3 0.7 POLYGON ((-118.20731 33.90754, -118.20641 33.9... 1 1 3 2
2326 Census Tract 1244 12.9 6.4 0.0 2.3 1.4 POLYGON ((-118.41379 34.17940, -118.41160 34.1... 1 1 0 0

2327 rows × 11 columns

In [11]:
df_trimmed['libraryaccess_classes']=df_trimmed[["Library_Access"]].apply(classifier)
df_trimmed

/opt/conda/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[11]:
Census_Tract Poverty Households_Without_Vehicle_Acce Transit_Access Outdoor_Workers Library_Access geometry poverty_classes vehicleaccess_classes transitaccess_classes outdoorwork_classes libraryaccess_classes
0 Census Tract 5709.02 1.5 3.8 0.0 5.6 0.8 POLYGON ((-118.12509 33.86032, -118.12287 33.8... 0 0 0 1 0
1 Census Tract 5715.02 10.0 9.0 98.3 2.5 1.2 POLYGON ((-118.19274 33.84376, -118.18975 33.8... 0 1 4 0 1
2 Census Tract 9106.05 17.1 3.3 0.0 11.6 2.9 POLYGON ((-118.07616 34.57281, -118.06728 34.5... 1 0 0 3 2
3 Census Tract 9107.12 7.0 0.9 0.0 3.6 4.2 POLYGON ((-118.05857 34.55809, -118.05847 34.5... 0 0 0 1 3
4 Census Tract 9107.15 11.3 8.9 0.0 4.6 3.1 POLYGON ((-118.08506 34.55654, -118.08485 34.5... 1 1 0 1 2
... ... ... ... ... ... ... ... ... ... ... ... ...
2322 Census Tract 6010.02 19.6 12.4 100.0 7.1 0.7 POLYGON ((-118.35263 33.95271, -118.35208 33.9... 2 2 4 2 0
2323 Census Tract 9005.01 28.7 4.9 0.0 7.3 1.5 POLYGON ((-118.13480 34.68936, -118.12968 34.6... 2 0 0 2 1
2324 Census Tract 9200.29 5.4 3.7 0.0 2.7 1.6 POLYGON ((-118.50373 34.42607, -118.50050 34.4... 0 0 0 0 1
2325 Census Tract 5420 13.3 8.7 88.0 9.3 0.7 POLYGON ((-118.20731 33.90754, -118.20641 33.9... 1 1 3 2 0
2326 Census Tract 1244 12.9 6.4 0.0 2.3 1.4 POLYGON ((-118.41379 34.17940, -118.41160 34.1... 1 1 0 0 1

2327 rows × 12 columns

In [12]:
df_trimmed['Heat_Score']=df_trimmed['poverty_classes']+df_trimmed['vehicleaccess_classes']+df_trimmed['transitaccess_classes']+df_trimmed['outdoorwork_classes']+df_trimmed['libraryaccess_classes']
df_trimmed

/opt/conda/lib/python3.10/site-packages/geopandas/geodataframe.py:1443: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
Out[12]:
Census_Tract Poverty Households_Without_Vehicle_Acce Transit_Access Outdoor_Workers Library_Access geometry poverty_classes vehicleaccess_classes transitaccess_classes outdoorwork_classes libraryaccess_classes Heat_Score
0 Census Tract 5709.02 1.5 3.8 0.0 5.6 0.8 POLYGON ((-118.12509 33.86032, -118.12287 33.8... 0 0 0 1 0 1
1 Census Tract 5715.02 10.0 9.0 98.3 2.5 1.2 POLYGON ((-118.19274 33.84376, -118.18975 33.8... 0 1 4 0 1 6
2 Census Tract 9106.05 17.1 3.3 0.0 11.6 2.9 POLYGON ((-118.07616 34.57281, -118.06728 34.5... 1 0 0 3 2 6
3 Census Tract 9107.12 7.0 0.9 0.0 3.6 4.2 POLYGON ((-118.05857 34.55809, -118.05847 34.5... 0 0 0 1 3 4
4 Census Tract 9107.15 11.3 8.9 0.0 4.6 3.1 POLYGON ((-118.08506 34.55654, -118.08485 34.5... 1 1 0 1 2 5
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2322 Census Tract 6010.02 19.6 12.4 100.0 7.1 0.7 POLYGON ((-118.35263 33.95271, -118.35208 33.9... 2 2 4 2 0 10
2323 Census Tract 9005.01 28.7 4.9 0.0 7.3 1.5 POLYGON ((-118.13480 34.68936, -118.12968 34.6... 2 0 0 2 1 5
2324 Census Tract 9200.29 5.4 3.7 0.0 2.7 1.6 POLYGON ((-118.50373 34.42607, -118.50050 34.4... 0 0 0 0 1 1
2325 Census Tract 5420 13.3 8.7 88.0 9.3 0.7 POLYGON ((-118.20731 33.90754, -118.20641 33.9... 1 1 3 2 0 7
2326 Census Tract 1244 12.9 6.4 0.0 2.3 1.4 POLYGON ((-118.41379 34.17940, -118.41160 34.1... 1 1 0 0 1 3

2327 rows × 13 columns

In [16]:
# Mapping
m=folium.Map(location=[34.2,-118.2],
             zoom_start=9,
             tiles='CartoDB positron',
             attribute='CartoDB')
In [17]:
style1 = {'fillOpacity': '0.0',
          'stroke': True,
          'weight': '3'}

folium. GeoJson ('Los_Angeles_County_CVA_Social_Vulnerability_Index.geojson',
                highlight_function=None, 
                style_function=lambda x:style1, 
                name="City", 
                overlay=True, 
                control=True, 
                show=False, 
                smooth_factor=None, 
                tooltip=None,
                embed=True, 
                popup=None, 
                zoom_on_click=True, 
                marker=None
                ).add_to (m)
Out[17]:
<folium.features.GeoJson at 0x7fe35ff94940>
In [22]:
folium.Choropleth(
geo_data=df_trimmed,
    data=df_trimmed,
    key_on='feature.properties.Census_Tract',
    columns=['Census_Tract', 'Heat_Score'],
    fill_color='YlOrRd',
    line_weight=0.8,
    fill_opacity=1.0,
    line_opacity=0.4,
    legend_name='Heat Vulnerability Index',
    name='Heat Vulnerability Index',  
    reset=True,
    
).add_to (m)

folium.LayerControl().add_to (m)
m
Out[22]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [23]:
#Make this Notebook Trusted (File>Trust Notebook)
jupyter trust /up221-amanda/Week07/week 10 assignment.ipynb

  Cell In[23], line 2
    jupyter trust /up221-amanda/Week07/week 10 assignment.ipynb
            ^
SyntaxError: invalid syntax
In [ ]: